Studying annotations over time
Federico Marini, Maria Stepanyan, Christoph
Fritzsch, Patrick Sorn
The Expressionists :)
1 Reading in of all infos for one file
We will focus on control siRNA_1 as a starter
list.files("kallisto_results/", recursive = TRUE)## [1] "v100/Control_siRNA_1/abundance.tsv" "v100/Control_siRNA_1/gene_expr.csv"
## [3] "v100/Control_siRNA_2/abundance.tsv" "v100/Control_siRNA_2/gene_expr.csv"
## [5] "v100/Control_siRNA_3/abundance.tsv" "v100/Control_siRNA_3/gene_expr.csv"
## [7] "v100/STAT5A_siRNA_1/abundance.tsv" "v100/STAT5A_siRNA_1/gene_expr.csv"
## [9] "v100/STAT5A_siRNA_2/abundance.tsv" "v100/STAT5A_siRNA_2/gene_expr.csv"
## [11] "v100/STAT5A_siRNA_3/abundance.tsv" "v100/STAT5A_siRNA_3/gene_expr.csv"
## [13] "v100/STAT5B_siRNA_1/abundance.tsv" "v100/STAT5B_siRNA_1/gene_expr.csv"
## [15] "v100/STAT5B_siRNA_2/abundance.tsv" "v100/STAT5B_siRNA_2/gene_expr.csv"
## [17] "v100/STAT5B_siRNA_3/abundance.tsv" "v100/STAT5B_siRNA_3/gene_expr.csv"
## [19] "v101/Control_siRNA_1/abundance.tsv" "v101/Control_siRNA_1/gene_expr.csv"
## [21] "v101/Control_siRNA_2/abundance.tsv" "v101/Control_siRNA_2/gene_expr.csv"
## [23] "v101/Control_siRNA_3/abundance.tsv" "v101/Control_siRNA_3/gene_expr.csv"
## [25] "v101/STAT5A_siRNA_1/abundance.tsv" "v101/STAT5A_siRNA_1/gene_expr.csv"
## [27] "v101/STAT5A_siRNA_2/abundance.tsv" "v101/STAT5A_siRNA_2/gene_expr.csv"
## [29] "v101/STAT5A_siRNA_3/abundance.tsv" "v101/STAT5A_siRNA_3/gene_expr.csv"
## [31] "v101/STAT5B_siRNA_1/abundance.tsv" "v101/STAT5B_siRNA_1/gene_expr.csv"
## [33] "v101/STAT5B_siRNA_2/abundance.tsv" "v101/STAT5B_siRNA_2/gene_expr.csv"
## [35] "v101/STAT5B_siRNA_3/abundance.tsv" "v101/STAT5B_siRNA_3/gene_expr.csv"
## [37] "v102/Control_siRNA_1/abundance.tsv" "v102/Control_siRNA_1/gene_expr.csv"
## [39] "v102/Control_siRNA_2/abundance.tsv" "v102/Control_siRNA_2/gene_expr.csv"
## [41] "v102/Control_siRNA_3/abundance.tsv" "v102/Control_siRNA_3/gene_expr.csv"
## [43] "v102/STAT5A_siRNA_1/abundance.tsv" "v102/STAT5A_siRNA_1/gene_expr.csv"
## [45] "v102/STAT5A_siRNA_2/abundance.tsv" "v102/STAT5A_siRNA_2/gene_expr.csv"
## [47] "v102/STAT5A_siRNA_3/abundance.tsv" "v102/STAT5A_siRNA_3/gene_expr.csv"
## [49] "v102/STAT5B_siRNA_1/abundance.tsv" "v102/STAT5B_siRNA_1/gene_expr.csv"
## [51] "v102/STAT5B_siRNA_2/abundance.tsv" "v102/STAT5B_siRNA_2/gene_expr.csv"
## [53] "v102/STAT5B_siRNA_3/abundance.tsv" "v102/STAT5B_siRNA_3/gene_expr.csv"
## [55] "v103/Control_siRNA_1/abundance.tsv" "v103/Control_siRNA_1/gene_expr.csv"
## [57] "v103/Control_siRNA_2/abundance.tsv" "v103/Control_siRNA_2/gene_expr.csv"
## [59] "v103/Control_siRNA_3/abundance.tsv" "v103/Control_siRNA_3/gene_expr.csv"
## [61] "v103/STAT5A_siRNA_1/abundance.tsv" "v103/STAT5A_siRNA_1/gene_expr.csv"
## [63] "v103/STAT5A_siRNA_2/abundance.tsv" "v103/STAT5A_siRNA_2/gene_expr.csv"
## [65] "v103/STAT5A_siRNA_3/abundance.tsv" "v103/STAT5A_siRNA_3/gene_expr.csv"
## [67] "v103/STAT5B_siRNA_1/abundance.tsv" "v103/STAT5B_siRNA_1/gene_expr.csv"
## [69] "v103/STAT5B_siRNA_2/abundance.tsv" "v103/STAT5B_siRNA_2/gene_expr.csv"
## [71] "v103/STAT5B_siRNA_3/abundance.tsv" "v103/STAT5B_siRNA_3/gene_expr.csv"
## [73] "v104/Control_siRNA_1/abundance.tsv" "v104/Control_siRNA_1/gene_expr.csv"
## [75] "v104/Control_siRNA_2/abundance.tsv" "v104/Control_siRNA_2/gene_expr.csv"
## [77] "v104/Control_siRNA_3/abundance.tsv" "v104/Control_siRNA_3/gene_expr.csv"
## [79] "v104/STAT5A_siRNA_1/abundance.tsv" "v104/STAT5A_siRNA_1/gene_expr.csv"
## [81] "v104/STAT5A_siRNA_2/abundance.tsv" "v104/STAT5A_siRNA_2/gene_expr.csv"
## [83] "v104/STAT5A_siRNA_3/abundance.tsv" "v104/STAT5A_siRNA_3/gene_expr.csv"
## [85] "v104/STAT5B_siRNA_1/abundance.tsv" "v104/STAT5B_siRNA_1/gene_expr.csv"
## [87] "v104/STAT5B_siRNA_2/abundance.tsv" "v104/STAT5B_siRNA_2/gene_expr.csv"
## [89] "v104/STAT5B_siRNA_3/abundance.tsv" "v104/STAT5B_siRNA_3/gene_expr.csv"
## [91] "v105/Control_siRNA_1/abundance.tsv" "v105/Control_siRNA_1/gene_expr.csv"
## [93] "v105/Control_siRNA_2/abundance.tsv" "v105/Control_siRNA_2/gene_expr.csv"
## [95] "v105/Control_siRNA_3/abundance.tsv" "v105/Control_siRNA_3/gene_expr.csv"
## [97] "v105/STAT5A_siRNA_1/abundance.tsv" "v105/STAT5A_siRNA_1/gene_expr.csv"
## [99] "v105/STAT5A_siRNA_2/abundance.tsv" "v105/STAT5A_siRNA_2/gene_expr.csv"
## [101] "v105/STAT5A_siRNA_3/abundance.tsv" "v105/STAT5A_siRNA_3/gene_expr.csv"
## [103] "v105/STAT5B_siRNA_1/abundance.tsv" "v105/STAT5B_siRNA_1/gene_expr.csv"
## [105] "v105/STAT5B_siRNA_2/abundance.tsv" "v105/STAT5B_siRNA_2/gene_expr.csv"
## [107] "v105/STAT5B_siRNA_3/abundance.tsv" "v105/STAT5B_siRNA_3/gene_expr.csv"
## [109] "v106/Control_siRNA_1/abundance.tsv" "v106/Control_siRNA_1/gene_expr.csv"
## [111] "v106/Control_siRNA_2/abundance.tsv" "v106/Control_siRNA_2/gene_expr.csv"
## [113] "v106/Control_siRNA_3/abundance.tsv" "v106/Control_siRNA_3/gene_expr.csv"
## [115] "v106/STAT5A_siRNA_1/abundance.tsv" "v106/STAT5A_siRNA_1/gene_expr.csv"
## [117] "v106/STAT5A_siRNA_2/abundance.tsv" "v106/STAT5A_siRNA_2/gene_expr.csv"
## [119] "v106/STAT5A_siRNA_3/abundance.tsv" "v106/STAT5A_siRNA_3/gene_expr.csv"
## [121] "v106/STAT5B_siRNA_1/abundance.tsv" "v106/STAT5B_siRNA_1/gene_expr.csv"
## [123] "v106/STAT5B_siRNA_2/abundance.tsv" "v106/STAT5B_siRNA_2/gene_expr.csv"
## [125] "v106/STAT5B_siRNA_3/abundance.tsv" "v106/STAT5B_siRNA_3/gene_expr.csv"
## [127] "v107/Control_siRNA_1/abundance.tsv" "v107/Control_siRNA_1/gene_expr.csv"
## [129] "v107/Control_siRNA_2/abundance.tsv" "v107/Control_siRNA_2/gene_expr.csv"
## [131] "v107/Control_siRNA_3/abundance.tsv" "v107/Control_siRNA_3/gene_expr.csv"
## [133] "v107/STAT5A_siRNA_1/abundance.tsv" "v107/STAT5A_siRNA_1/gene_expr.csv"
## [135] "v107/STAT5A_siRNA_2/abundance.tsv" "v107/STAT5A_siRNA_2/gene_expr.csv"
## [137] "v107/STAT5A_siRNA_3/abundance.tsv" "v107/STAT5A_siRNA_3/gene_expr.csv"
## [139] "v107/STAT5B_siRNA_1/abundance.tsv" "v107/STAT5B_siRNA_1/gene_expr.csv"
## [141] "v107/STAT5B_siRNA_2/abundance.tsv" "v107/STAT5B_siRNA_2/gene_expr.csv"
## [143] "v107/STAT5B_siRNA_3/abundance.tsv" "v107/STAT5B_siRNA_3/gene_expr.csv"
## [145] "v108/Control_siRNA_1/abundance.tsv" "v108/Control_siRNA_1/gene_expr.csv"
## [147] "v108/Control_siRNA_2/abundance.tsv" "v108/Control_siRNA_2/gene_expr.csv"
## [149] "v108/Control_siRNA_3/abundance.tsv" "v108/Control_siRNA_3/gene_expr.csv"
## [151] "v108/STAT5A_siRNA_1/abundance.tsv" "v108/STAT5A_siRNA_1/gene_expr.csv"
## [153] "v108/STAT5A_siRNA_2/abundance.tsv" "v108/STAT5A_siRNA_2/gene_expr.csv"
## [155] "v108/STAT5A_siRNA_3/abundance.tsv" "v108/STAT5A_siRNA_3/gene_expr.csv"
## [157] "v108/STAT5B_siRNA_1/abundance.tsv" "v108/STAT5B_siRNA_1/gene_expr.csv"
## [159] "v108/STAT5B_siRNA_2/abundance.tsv" "v108/STAT5B_siRNA_2/gene_expr.csv"
## [161] "v108/STAT5B_siRNA_3/abundance.tsv" "v108/STAT5B_siRNA_3/gene_expr.csv"
## [163] "v109/Control_siRNA_1/abundance.tsv" "v109/Control_siRNA_1/gene_expr.csv"
## [165] "v109/Control_siRNA_2/abundance.tsv" "v109/Control_siRNA_2/gene_expr.csv"
## [167] "v109/Control_siRNA_3/abundance.tsv" "v109/Control_siRNA_3/gene_expr.csv"
## [169] "v109/STAT5A_siRNA_1/abundance.tsv" "v109/STAT5A_siRNA_1/gene_expr.csv"
## [171] "v109/STAT5A_siRNA_2/abundance.tsv" "v109/STAT5A_siRNA_2/gene_expr.csv"
## [173] "v109/STAT5A_siRNA_3/abundance.tsv" "v109/STAT5A_siRNA_3/gene_expr.csv"
## [175] "v109/STAT5B_siRNA_1/abundance.tsv" "v109/STAT5B_siRNA_1/gene_expr.csv"
## [177] "v109/STAT5B_siRNA_2/abundance.tsv" "v109/STAT5B_siRNA_2/gene_expr.csv"
## [179] "v109/STAT5B_siRNA_3/abundance.tsv" "v109/STAT5B_siRNA_3/gene_expr.csv"
## [181] "v86/Control_siRNA_1/abundance.tsv" "v86/Control_siRNA_1/gene_expr.csv"
## [183] "v86/Control_siRNA_2/abundance.tsv" "v86/Control_siRNA_2/gene_expr.csv"
## [185] "v86/Control_siRNA_3/abundance.tsv" "v86/Control_siRNA_3/gene_expr.csv"
## [187] "v86/STAT5A_siRNA_1/abundance.tsv" "v86/STAT5A_siRNA_1/gene_expr.csv"
## [189] "v86/STAT5A_siRNA_2/abundance.tsv" "v86/STAT5A_siRNA_2/gene_expr.csv"
## [191] "v86/STAT5A_siRNA_3/abundance.tsv" "v86/STAT5A_siRNA_3/gene_expr.csv"
## [193] "v86/STAT5B_siRNA_1/abundance.tsv" "v86/STAT5B_siRNA_1/gene_expr.csv"
## [195] "v86/STAT5B_siRNA_2/abundance.tsv" "v86/STAT5B_siRNA_2/gene_expr.csv"
## [197] "v86/STAT5B_siRNA_3/abundance.tsv" "v86/STAT5B_siRNA_3/gene_expr.csv"
## [199] "v87/Control_siRNA_1/abundance.tsv" "v87/Control_siRNA_1/gene_expr.csv"
## [201] "v87/Control_siRNA_2/abundance.tsv" "v87/Control_siRNA_2/gene_expr.csv"
## [203] "v87/Control_siRNA_3/abundance.tsv" "v87/Control_siRNA_3/gene_expr.csv"
## [205] "v87/STAT5A_siRNA_1/abundance.tsv" "v87/STAT5A_siRNA_1/gene_expr.csv"
## [207] "v87/STAT5A_siRNA_2/abundance.tsv" "v87/STAT5A_siRNA_2/gene_expr.csv"
## [209] "v87/STAT5A_siRNA_3/abundance.tsv" "v87/STAT5A_siRNA_3/gene_expr.csv"
## [211] "v87/STAT5B_siRNA_1/abundance.tsv" "v87/STAT5B_siRNA_1/gene_expr.csv"
## [213] "v87/STAT5B_siRNA_2/abundance.tsv" "v87/STAT5B_siRNA_2/gene_expr.csv"
## [215] "v87/STAT5B_siRNA_3/abundance.tsv" "v87/STAT5B_siRNA_3/gene_expr.csv"
## [217] "v88/Control_siRNA_1/abundance.tsv" "v88/Control_siRNA_1/gene_expr.csv"
## [219] "v88/Control_siRNA_2/abundance.tsv" "v88/Control_siRNA_2/gene_expr.csv"
## [221] "v88/Control_siRNA_3/abundance.tsv" "v88/Control_siRNA_3/gene_expr.csv"
## [223] "v88/STAT5A_siRNA_1/abundance.tsv" "v88/STAT5A_siRNA_1/gene_expr.csv"
## [225] "v88/STAT5A_siRNA_2/abundance.tsv" "v88/STAT5A_siRNA_2/gene_expr.csv"
## [227] "v88/STAT5A_siRNA_3/abundance.tsv" "v88/STAT5A_siRNA_3/gene_expr.csv"
## [229] "v88/STAT5B_siRNA_1/abundance.tsv" "v88/STAT5B_siRNA_1/gene_expr.csv"
## [231] "v88/STAT5B_siRNA_2/abundance.tsv" "v88/STAT5B_siRNA_2/gene_expr.csv"
## [233] "v88/STAT5B_siRNA_3/abundance.tsv" "v88/STAT5B_siRNA_3/gene_expr.csv"
## [235] "v89/Control_siRNA_1/abundance.tsv" "v89/Control_siRNA_1/gene_expr.csv"
## [237] "v89/Control_siRNA_2/abundance.tsv" "v89/Control_siRNA_2/gene_expr.csv"
## [239] "v89/Control_siRNA_3/abundance.tsv" "v89/Control_siRNA_3/gene_expr.csv"
## [241] "v89/STAT5A_siRNA_1/abundance.tsv" "v89/STAT5A_siRNA_1/gene_expr.csv"
## [243] "v89/STAT5A_siRNA_2/abundance.tsv" "v89/STAT5A_siRNA_2/gene_expr.csv"
## [245] "v89/STAT5A_siRNA_3/abundance.tsv" "v89/STAT5A_siRNA_3/gene_expr.csv"
## [247] "v89/STAT5B_siRNA_1/abundance.tsv" "v89/STAT5B_siRNA_1/gene_expr.csv"
## [249] "v89/STAT5B_siRNA_2/abundance.tsv" "v89/STAT5B_siRNA_2/gene_expr.csv"
## [251] "v89/STAT5B_siRNA_3/abundance.tsv" "v89/STAT5B_siRNA_3/gene_expr.csv"
## [253] "v90/Control_siRNA_1/abundance.tsv" "v90/Control_siRNA_1/gene_expr.csv"
## [255] "v90/Control_siRNA_2/abundance.tsv" "v90/Control_siRNA_2/gene_expr.csv"
## [257] "v90/Control_siRNA_3/abundance.tsv" "v90/Control_siRNA_3/gene_expr.csv"
## [259] "v90/STAT5A_siRNA_1/abundance.tsv" "v90/STAT5A_siRNA_1/gene_expr.csv"
## [261] "v90/STAT5A_siRNA_2/abundance.tsv" "v90/STAT5A_siRNA_2/gene_expr.csv"
## [263] "v90/STAT5A_siRNA_3/abundance.tsv" "v90/STAT5A_siRNA_3/gene_expr.csv"
## [265] "v90/STAT5B_siRNA_1/abundance.tsv" "v90/STAT5B_siRNA_1/gene_expr.csv"
## [267] "v90/STAT5B_siRNA_2/abundance.tsv" "v90/STAT5B_siRNA_2/gene_expr.csv"
## [269] "v90/STAT5B_siRNA_3/abundance.tsv" "v90/STAT5B_siRNA_3/gene_expr.csv"
## [271] "v91/Control_siRNA_1/abundance.tsv" "v91/Control_siRNA_1/gene_expr.csv"
## [273] "v91/Control_siRNA_2/abundance.tsv" "v91/Control_siRNA_2/gene_expr.csv"
## [275] "v91/Control_siRNA_3/abundance.tsv" "v91/Control_siRNA_3/gene_expr.csv"
## [277] "v91/STAT5A_siRNA_1/abundance.tsv" "v91/STAT5A_siRNA_1/gene_expr.csv"
## [279] "v91/STAT5A_siRNA_2/abundance.tsv" "v91/STAT5A_siRNA_2/gene_expr.csv"
## [281] "v91/STAT5A_siRNA_3/abundance.tsv" "v91/STAT5A_siRNA_3/gene_expr.csv"
## [283] "v91/STAT5B_siRNA_1/abundance.tsv" "v91/STAT5B_siRNA_1/gene_expr.csv"
## [285] "v91/STAT5B_siRNA_2/abundance.tsv" "v91/STAT5B_siRNA_2/gene_expr.csv"
## [287] "v91/STAT5B_siRNA_3/abundance.tsv" "v91/STAT5B_siRNA_3/gene_expr.csv"
## [289] "v92/Control_siRNA_1/abundance.tsv" "v92/Control_siRNA_1/gene_expr.csv"
## [291] "v92/Control_siRNA_2/abundance.tsv" "v92/Control_siRNA_2/gene_expr.csv"
## [293] "v92/Control_siRNA_3/abundance.tsv" "v92/Control_siRNA_3/gene_expr.csv"
## [295] "v92/STAT5A_siRNA_1/abundance.tsv" "v92/STAT5A_siRNA_1/gene_expr.csv"
## [297] "v92/STAT5A_siRNA_2/abundance.tsv" "v92/STAT5A_siRNA_2/gene_expr.csv"
## [299] "v92/STAT5A_siRNA_3/abundance.tsv" "v92/STAT5A_siRNA_3/gene_expr.csv"
## [301] "v92/STAT5B_siRNA_1/abundance.tsv" "v92/STAT5B_siRNA_1/gene_expr.csv"
## [303] "v92/STAT5B_siRNA_2/abundance.tsv" "v92/STAT5B_siRNA_2/gene_expr.csv"
## [305] "v92/STAT5B_siRNA_3/abundance.tsv" "v92/STAT5B_siRNA_3/gene_expr.csv"
## [307] "v93/Control_siRNA_1/abundance.tsv" "v93/Control_siRNA_1/gene_expr.csv"
## [309] "v93/Control_siRNA_2/abundance.tsv" "v93/Control_siRNA_2/gene_expr.csv"
## [311] "v93/Control_siRNA_3/abundance.tsv" "v93/Control_siRNA_3/gene_expr.csv"
## [313] "v93/STAT5A_siRNA_1/abundance.tsv" "v93/STAT5A_siRNA_1/gene_expr.csv"
## [315] "v93/STAT5A_siRNA_2/abundance.tsv" "v93/STAT5A_siRNA_2/gene_expr.csv"
## [317] "v93/STAT5A_siRNA_3/abundance.tsv" "v93/STAT5A_siRNA_3/gene_expr.csv"
## [319] "v93/STAT5B_siRNA_1/abundance.tsv" "v93/STAT5B_siRNA_1/gene_expr.csv"
## [321] "v93/STAT5B_siRNA_2/abundance.tsv" "v93/STAT5B_siRNA_2/gene_expr.csv"
## [323] "v93/STAT5B_siRNA_3/abundance.tsv" "v93/STAT5B_siRNA_3/gene_expr.csv"
## [325] "v94/Control_siRNA_1/abundance.tsv" "v94/Control_siRNA_1/gene_expr.csv"
## [327] "v94/Control_siRNA_2/abundance.tsv" "v94/Control_siRNA_2/gene_expr.csv"
## [329] "v94/Control_siRNA_3/abundance.tsv" "v94/Control_siRNA_3/gene_expr.csv"
## [331] "v94/STAT5A_siRNA_1/abundance.tsv" "v94/STAT5A_siRNA_1/gene_expr.csv"
## [333] "v94/STAT5A_siRNA_2/abundance.tsv" "v94/STAT5A_siRNA_2/gene_expr.csv"
## [335] "v94/STAT5A_siRNA_3/abundance.tsv" "v94/STAT5A_siRNA_3/gene_expr.csv"
## [337] "v94/STAT5B_siRNA_1/abundance.tsv" "v94/STAT5B_siRNA_1/gene_expr.csv"
## [339] "v94/STAT5B_siRNA_2/abundance.tsv" "v94/STAT5B_siRNA_2/gene_expr.csv"
## [341] "v94/STAT5B_siRNA_3/abundance.tsv" "v94/STAT5B_siRNA_3/gene_expr.csv"
## [343] "v95/Control_siRNA_1/abundance.tsv" "v95/Control_siRNA_1/gene_expr.csv"
## [345] "v95/Control_siRNA_2/abundance.tsv" "v95/Control_siRNA_2/gene_expr.csv"
## [347] "v95/Control_siRNA_3/abundance.tsv" "v95/Control_siRNA_3/gene_expr.csv"
## [349] "v95/STAT5A_siRNA_1/abundance.tsv" "v95/STAT5A_siRNA_1/gene_expr.csv"
## [351] "v95/STAT5A_siRNA_2/abundance.tsv" "v95/STAT5A_siRNA_2/gene_expr.csv"
## [353] "v95/STAT5A_siRNA_3/abundance.tsv" "v95/STAT5A_siRNA_3/gene_expr.csv"
## [355] "v95/STAT5B_siRNA_1/abundance.tsv" "v95/STAT5B_siRNA_1/gene_expr.csv"
## [357] "v95/STAT5B_siRNA_2/abundance.tsv" "v95/STAT5B_siRNA_2/gene_expr.csv"
## [359] "v95/STAT5B_siRNA_3/abundance.tsv" "v95/STAT5B_siRNA_3/gene_expr.csv"
## [361] "v96/Control_siRNA_1/abundance.tsv" "v96/Control_siRNA_1/gene_expr.csv"
## [363] "v96/Control_siRNA_2/abundance.tsv" "v96/Control_siRNA_2/gene_expr.csv"
## [365] "v96/Control_siRNA_3/abundance.tsv" "v96/Control_siRNA_3/gene_expr.csv"
## [367] "v96/STAT5A_siRNA_1/abundance.tsv" "v96/STAT5A_siRNA_1/gene_expr.csv"
## [369] "v96/STAT5A_siRNA_2/abundance.tsv" "v96/STAT5A_siRNA_2/gene_expr.csv"
## [371] "v96/STAT5A_siRNA_3/abundance.tsv" "v96/STAT5A_siRNA_3/gene_expr.csv"
## [373] "v96/STAT5B_siRNA_1/abundance.tsv" "v96/STAT5B_siRNA_1/gene_expr.csv"
## [375] "v96/STAT5B_siRNA_2/abundance.tsv" "v96/STAT5B_siRNA_2/gene_expr.csv"
## [377] "v96/STAT5B_siRNA_3/abundance.tsv" "v96/STAT5B_siRNA_3/gene_expr.csv"
## [379] "v97/Control_siRNA_1/abundance.tsv" "v97/Control_siRNA_1/gene_expr.csv"
## [381] "v97/Control_siRNA_2/abundance.tsv" "v97/Control_siRNA_2/gene_expr.csv"
## [383] "v97/Control_siRNA_3/abundance.tsv" "v97/Control_siRNA_3/gene_expr.csv"
## [385] "v97/STAT5A_siRNA_1/abundance.tsv" "v97/STAT5A_siRNA_1/gene_expr.csv"
## [387] "v97/STAT5A_siRNA_2/abundance.tsv" "v97/STAT5A_siRNA_2/gene_expr.csv"
## [389] "v97/STAT5A_siRNA_3/abundance.tsv" "v97/STAT5A_siRNA_3/gene_expr.csv"
## [391] "v97/STAT5B_siRNA_1/abundance.tsv" "v97/STAT5B_siRNA_1/gene_expr.csv"
## [393] "v97/STAT5B_siRNA_2/abundance.tsv" "v97/STAT5B_siRNA_2/gene_expr.csv"
## [395] "v97/STAT5B_siRNA_3/abundance.tsv" "v97/STAT5B_siRNA_3/gene_expr.csv"
## [397] "v98/Control_siRNA_1/abundance.tsv" "v98/Control_siRNA_1/gene_expr.csv"
## [399] "v98/Control_siRNA_2/abundance.tsv" "v98/Control_siRNA_2/gene_expr.csv"
## [401] "v98/Control_siRNA_3/abundance.tsv" "v98/Control_siRNA_3/gene_expr.csv"
## [403] "v98/STAT5A_siRNA_1/abundance.tsv" "v98/STAT5A_siRNA_1/gene_expr.csv"
## [405] "v98/STAT5A_siRNA_2/abundance.tsv" "v98/STAT5A_siRNA_2/gene_expr.csv"
## [407] "v98/STAT5A_siRNA_3/abundance.tsv" "v98/STAT5A_siRNA_3/gene_expr.csv"
## [409] "v98/STAT5B_siRNA_1/abundance.tsv" "v98/STAT5B_siRNA_1/gene_expr.csv"
## [411] "v98/STAT5B_siRNA_2/abundance.tsv" "v98/STAT5B_siRNA_2/gene_expr.csv"
## [413] "v98/STAT5B_siRNA_3/abundance.tsv" "v98/STAT5B_siRNA_3/gene_expr.csv"
## [415] "v99/Control_siRNA_1/abundance.tsv" "v99/Control_siRNA_1/gene_expr.csv"
## [417] "v99/Control_siRNA_2/abundance.tsv" "v99/Control_siRNA_2/gene_expr.csv"
## [419] "v99/Control_siRNA_3/abundance.tsv" "v99/Control_siRNA_3/gene_expr.csv"
## [421] "v99/STAT5A_siRNA_1/abundance.tsv" "v99/STAT5A_siRNA_1/gene_expr.csv"
## [423] "v99/STAT5A_siRNA_2/abundance.tsv" "v99/STAT5A_siRNA_2/gene_expr.csv"
## [425] "v99/STAT5A_siRNA_3/abundance.tsv" "v99/STAT5A_siRNA_3/gene_expr.csv"
## [427] "v99/STAT5B_siRNA_1/abundance.tsv" "v99/STAT5B_siRNA_1/gene_expr.csv"
## [429] "v99/STAT5B_siRNA_2/abundance.tsv" "v99/STAT5B_siRNA_2/gene_expr.csv"
## [431] "v99/STAT5B_siRNA_3/abundance.tsv" "v99/STAT5B_siRNA_3/gene_expr.csv"gene_counts_ctrl1_v86 <- read.csv("kallisto_results/v86/Control_siRNA_1/gene_expr.csv", sep = ";")
tx_counts_ctrl1_v86 <- read.csv("kallisto_results/v86/Control_siRNA_1/abundance.tsv", sep = "\t")
# View(gene_counts_ctrl1_v86)
# View(tx_counts_ctrl1_v86)
all_gene_files <- list.files("kallisto_results", pattern = "gene_expr.csv",
recursive = TRUE, full.names = TRUE)
all_sample1_gene_files <- all_gene_files[grep("Control_siRNA_1", all_gene_files)]
all_sample1_gene_files## [1] "kallisto_results/v100/Control_siRNA_1/gene_expr.csv"
## [2] "kallisto_results/v101/Control_siRNA_1/gene_expr.csv"
## [3] "kallisto_results/v102/Control_siRNA_1/gene_expr.csv"
## [4] "kallisto_results/v103/Control_siRNA_1/gene_expr.csv"
## [5] "kallisto_results/v104/Control_siRNA_1/gene_expr.csv"
## [6] "kallisto_results/v105/Control_siRNA_1/gene_expr.csv"
## [7] "kallisto_results/v106/Control_siRNA_1/gene_expr.csv"
## [8] "kallisto_results/v107/Control_siRNA_1/gene_expr.csv"
## [9] "kallisto_results/v108/Control_siRNA_1/gene_expr.csv"
## [10] "kallisto_results/v109/Control_siRNA_1/gene_expr.csv"
## [11] "kallisto_results/v86/Control_siRNA_1/gene_expr.csv"
## [12] "kallisto_results/v87/Control_siRNA_1/gene_expr.csv"
## [13] "kallisto_results/v88/Control_siRNA_1/gene_expr.csv"
## [14] "kallisto_results/v89/Control_siRNA_1/gene_expr.csv"
## [15] "kallisto_results/v90/Control_siRNA_1/gene_expr.csv"
## [16] "kallisto_results/v91/Control_siRNA_1/gene_expr.csv"
## [17] "kallisto_results/v92/Control_siRNA_1/gene_expr.csv"
## [18] "kallisto_results/v93/Control_siRNA_1/gene_expr.csv"
## [19] "kallisto_results/v94/Control_siRNA_1/gene_expr.csv"
## [20] "kallisto_results/v95/Control_siRNA_1/gene_expr.csv"
## [21] "kallisto_results/v96/Control_siRNA_1/gene_expr.csv"
## [22] "kallisto_results/v97/Control_siRNA_1/gene_expr.csv"
## [23] "kallisto_results/v98/Control_siRNA_1/gene_expr.csv"
## [24] "kallisto_results/v99/Control_siRNA_1/gene_expr.csv"all_sample1_gene_files <- all_sample1_gene_files[c(c(11:24), c(1:10))]
all_sample1_gene_files## [1] "kallisto_results/v86/Control_siRNA_1/gene_expr.csv"
## [2] "kallisto_results/v87/Control_siRNA_1/gene_expr.csv"
## [3] "kallisto_results/v88/Control_siRNA_1/gene_expr.csv"
## [4] "kallisto_results/v89/Control_siRNA_1/gene_expr.csv"
## [5] "kallisto_results/v90/Control_siRNA_1/gene_expr.csv"
## [6] "kallisto_results/v91/Control_siRNA_1/gene_expr.csv"
## [7] "kallisto_results/v92/Control_siRNA_1/gene_expr.csv"
## [8] "kallisto_results/v93/Control_siRNA_1/gene_expr.csv"
## [9] "kallisto_results/v94/Control_siRNA_1/gene_expr.csv"
## [10] "kallisto_results/v95/Control_siRNA_1/gene_expr.csv"
## [11] "kallisto_results/v96/Control_siRNA_1/gene_expr.csv"
## [12] "kallisto_results/v97/Control_siRNA_1/gene_expr.csv"
## [13] "kallisto_results/v98/Control_siRNA_1/gene_expr.csv"
## [14] "kallisto_results/v99/Control_siRNA_1/gene_expr.csv"
## [15] "kallisto_results/v100/Control_siRNA_1/gene_expr.csv"
## [16] "kallisto_results/v101/Control_siRNA_1/gene_expr.csv"
## [17] "kallisto_results/v102/Control_siRNA_1/gene_expr.csv"
## [18] "kallisto_results/v103/Control_siRNA_1/gene_expr.csv"
## [19] "kallisto_results/v104/Control_siRNA_1/gene_expr.csv"
## [20] "kallisto_results/v105/Control_siRNA_1/gene_expr.csv"
## [21] "kallisto_results/v106/Control_siRNA_1/gene_expr.csv"
## [22] "kallisto_results/v107/Control_siRNA_1/gene_expr.csv"
## [23] "kallisto_results/v108/Control_siRNA_1/gene_expr.csv"
## [24] "kallisto_results/v109/Control_siRNA_1/gene_expr.csv"all_tx_files <- list.files("kallisto_results", pattern = "abundance.tsv",
recursive = TRUE, full.names = TRUE)
all_sample1_tx_files <- all_tx_files[grep("Control_siRNA_1", all_tx_files)]
all_sample1_tx_files## [1] "kallisto_results/v100/Control_siRNA_1/abundance.tsv"
## [2] "kallisto_results/v101/Control_siRNA_1/abundance.tsv"
## [3] "kallisto_results/v102/Control_siRNA_1/abundance.tsv"
## [4] "kallisto_results/v103/Control_siRNA_1/abundance.tsv"
## [5] "kallisto_results/v104/Control_siRNA_1/abundance.tsv"
## [6] "kallisto_results/v105/Control_siRNA_1/abundance.tsv"
## [7] "kallisto_results/v106/Control_siRNA_1/abundance.tsv"
## [8] "kallisto_results/v107/Control_siRNA_1/abundance.tsv"
## [9] "kallisto_results/v108/Control_siRNA_1/abundance.tsv"
## [10] "kallisto_results/v109/Control_siRNA_1/abundance.tsv"
## [11] "kallisto_results/v86/Control_siRNA_1/abundance.tsv"
## [12] "kallisto_results/v87/Control_siRNA_1/abundance.tsv"
## [13] "kallisto_results/v88/Control_siRNA_1/abundance.tsv"
## [14] "kallisto_results/v89/Control_siRNA_1/abundance.tsv"
## [15] "kallisto_results/v90/Control_siRNA_1/abundance.tsv"
## [16] "kallisto_results/v91/Control_siRNA_1/abundance.tsv"
## [17] "kallisto_results/v92/Control_siRNA_1/abundance.tsv"
## [18] "kallisto_results/v93/Control_siRNA_1/abundance.tsv"
## [19] "kallisto_results/v94/Control_siRNA_1/abundance.tsv"
## [20] "kallisto_results/v95/Control_siRNA_1/abundance.tsv"
## [21] "kallisto_results/v96/Control_siRNA_1/abundance.tsv"
## [22] "kallisto_results/v97/Control_siRNA_1/abundance.tsv"
## [23] "kallisto_results/v98/Control_siRNA_1/abundance.tsv"
## [24] "kallisto_results/v99/Control_siRNA_1/abundance.tsv"all_sample1_tx_files <- all_sample1_tx_files[c(c(11:24), c(1:10))]
all_sample1_tx_files## [1] "kallisto_results/v86/Control_siRNA_1/abundance.tsv"
## [2] "kallisto_results/v87/Control_siRNA_1/abundance.tsv"
## [3] "kallisto_results/v88/Control_siRNA_1/abundance.tsv"
## [4] "kallisto_results/v89/Control_siRNA_1/abundance.tsv"
## [5] "kallisto_results/v90/Control_siRNA_1/abundance.tsv"
## [6] "kallisto_results/v91/Control_siRNA_1/abundance.tsv"
## [7] "kallisto_results/v92/Control_siRNA_1/abundance.tsv"
## [8] "kallisto_results/v93/Control_siRNA_1/abundance.tsv"
## [9] "kallisto_results/v94/Control_siRNA_1/abundance.tsv"
## [10] "kallisto_results/v95/Control_siRNA_1/abundance.tsv"
## [11] "kallisto_results/v96/Control_siRNA_1/abundance.tsv"
## [12] "kallisto_results/v97/Control_siRNA_1/abundance.tsv"
## [13] "kallisto_results/v98/Control_siRNA_1/abundance.tsv"
## [14] "kallisto_results/v99/Control_siRNA_1/abundance.tsv"
## [15] "kallisto_results/v100/Control_siRNA_1/abundance.tsv"
## [16] "kallisto_results/v101/Control_siRNA_1/abundance.tsv"
## [17] "kallisto_results/v102/Control_siRNA_1/abundance.tsv"
## [18] "kallisto_results/v103/Control_siRNA_1/abundance.tsv"
## [19] "kallisto_results/v104/Control_siRNA_1/abundance.tsv"
## [20] "kallisto_results/v105/Control_siRNA_1/abundance.tsv"
## [21] "kallisto_results/v106/Control_siRNA_1/abundance.tsv"
## [22] "kallisto_results/v107/Control_siRNA_1/abundance.tsv"
## [23] "kallisto_results/v108/Control_siRNA_1/abundance.tsv"
## [24] "kallisto_results/v109/Control_siRNA_1/abundance.tsv"1.1 Some quantifications of things over time
genes_over_time <- lapply(1:24, function(arg) {
gene_file <- read.csv(all_sample1_gene_files[arg], sep = ";")
n_genes <- nrow(gene_file)
tx_file <- read.csv(all_sample1_tx_files[arg], sep = "\t")
n_transcripts <- nrow(tx_file)
gene_ids <- gene_file$gene_id
gene_names <- gene_file$gene_symbol
tx_ids <- tx_file$target_id
out <- list(
n_genes = n_genes,
n_transcripts = n_transcripts,
gene_ids = gene_ids,
gene_names = gene_names,
tx_ids = tx_ids
)
})
names(genes_over_time) <- (list.files("kallisto_results", recursive = FALSE))[c(c(11:24), c(1:10))]
anns_df <- data.frame(
version = factor(names(genes_over_time), levels = names(genes_over_time)),
n_genes = sapply(genes_over_time, function(arg) arg$n_genes),
n_transcripts = sapply(genes_over_time, function(arg) arg$n_transcripts)
)
anns_df## version n_genes n_transcripts
## v86 v86 34983 178136
## v87 v87 34983 178136
## v88 v88 34835 179973
## v89 v89 34835 179973
## v90 v90 34912 180869
## v91 v91 34912 180869
## v92 v92 35004 185299
## v93 v93 35004 185299
## v94 v94 35548 187626
## v95 v95 35548 187626
## v96 v96 35571 188753
## v97 v97 35593 189154
## v98 v98 35593 190069
## v99 v99 35604 190432
## v100 v100 35602 190522
## v101 v101 35598 191887
## v102 v102 35601 194360
## v103 v103 35602 196722
## v104 v104 35606 199240
## v105 v105 35623 202897
## v106 v106 35640 204563
## v107 v107 35632 207877
## v108 v108 35646 205131
## v109 v109 35658 205541Plotting these things a bit
library("ggplot2")
ggplot(anns_df,
aes(x = version, y = n_genes)) +
coord_cartesian(ylim = c(0, NA)) +
geom_point() +
theme_bw() +
ggtitle("Number of genes over time", subtitle = "v86 to v109")
ggplot(anns_df,
aes(x = version, y = n_genes)) +
geom_point() +
theme_bw() +
ggtitle("Number of genes over time", subtitle = "v86 to v109")
ggplot(anns_df,
aes(x = version, y = n_transcripts)) +
coord_cartesian(ylim = c(0, NA)) +
geom_point() +
theme_bw() +
ggtitle("Number of transcripts over time", subtitle = "v86 to v109")
ggplot(anns_df,
aes(x = version, y = n_transcripts)) +
geom_point() +
theme_bw() +
ggtitle("Number of transcripts over time", subtitle = "v86 to v109")
Working a bit more on the “real content” of the genes included in the annotations
- sampling over the whole period
gplots::venn(
list(
v86 = genes_over_time$v86$gene_ids,
v94 = genes_over_time$v94$gene_ids,
v102 = genes_over_time$v102$gene_ids,
v109 = genes_over_time$v109$gene_ids
)
)
gplots::venn(
list(
v86 = genes_over_time$v86$gene_names,
v94 = genes_over_time$v94$gene_names,
v102 = genes_over_time$v102$gene_names,
v109 = genes_over_time$v109$gene_names
)
)
- sampling a little bit more close over time
gplots::venn(
list(
v86 = genes_over_time$v86$gene_ids,
v87 = genes_over_time$v87$gene_ids,
v88 = genes_over_time$v88$gene_ids,
v89 = genes_over_time$v89$gene_ids
)
)
Let’s take this a little bit next level, with an Upset plot
UpSetR::upset(
UpSetR::fromList(
list(
v86 = genes_over_time$v86$gene_ids,
v94 = genes_over_time$v94$gene_ids,
v102 = genes_over_time$v102$gene_ids,
v109 = genes_over_time$v109$gene_ids
)
)
)
UpSetR::upset(
UpSetR::fromList(
list(
v86 = genes_over_time$v86$gene_ids,
v88 = genes_over_time$v88$gene_ids,
v90 = genes_over_time$v90$gene_ids,
v92 = genes_over_time$v92$gene_ids,
v94 = genes_over_time$v94$gene_ids,
v96 = genes_over_time$v96$gene_ids,
v98 = genes_over_time$v98$gene_ids,
v100 = genes_over_time$v100$gene_ids,
v102 = genes_over_time$v102$gene_ids,
v104 = genes_over_time$v104$gene_ids,
v106 = genes_over_time$v106$gene_ids,
v109 = genes_over_time$v109$gene_ids
)
), nintersects = NA, nsets = 12
)
2 DE over time
DE with oldest version
all_samples_v86 <- file.path(
list.files(path = "kallisto_results/v86",
full.names = TRUE),
"gene_expr.csv")
file.exists(all_samples_v86)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEcm_v86 <- data.frame(
ctrl_r1 = read.csv(all_samples_v86[1], sep = ";")$count,
ctrl_r2 = read.csv(all_samples_v86[2], sep = ";")$count,
ctrl_r3 = read.csv(all_samples_v86[3], sep = ";")$count,
stat5a_r1 = read.csv(all_samples_v86[4], sep = ";")$count,
stat5a_r2 = read.csv(all_samples_v86[5], sep = ";")$count,
stat5a_r3 = read.csv(all_samples_v86[6], sep = ";")$count,
stat5b_r1 = read.csv(all_samples_v86[7], sep = ";")$count,
stat5b_r2 = read.csv(all_samples_v86[8], sep = ";")$count,
stat5b_r3 = read.csv(all_samples_v86[9], sep = ";")$count,
row.names = read.csv(all_samples_v86[1], sep = ";")$gene_id
)
anno_v86 <- data.frame(
gene_id = read.csv(all_samples_v86[1], sep = ";")$gene_id,
gene_name = read.csv(all_samples_v86[1], sep = ";")$gene_symbol,
row.names = read.csv(all_samples_v86[1], sep = ";")$gene_id
)
head(cm_v86)## ctrl_r1 ctrl_r2 ctrl_r3 stat5a_r1 stat5a_r2 stat5a_r3 stat5b_r1
## ENSG00000000003 4 3 0 5 2 7 1
## ENSG00000000005 0 0 0 0 0 0 0
## ENSG00000000419 0 1 0 2 1 0 0
## ENSG00000000457 295 216 250 282 390 333 246
## ENSG00000000460 140 116 125 99 107 83 137
## ENSG00000000938 10111 7849 9162 13445 12557 10639 10144
## stat5b_r2 stat5b_r3
## ENSG00000000003 2 2
## ENSG00000000005 0 0
## ENSG00000000419 0 0
## ENSG00000000457 246 309
## ENSG00000000460 157 106
## ENSG00000000938 10895 10169dim(cm_v86)## [1] 34983 9head(anno_v86)## gene_id gene_name
## ENSG00000000003 ENSG00000000003 TSPAN6
## ENSG00000000005 ENSG00000000005 TNMD
## ENSG00000000419 ENSG00000000419 DPM1
## ENSG00000000457 ENSG00000000457 SCYL3
## ENSG00000000460 ENSG00000000460 C1orf112
## ENSG00000000938 ENSG00000000938 FGRsamples_metadata <- data.frame(
group = c("ctrl", "ctrl", "ctrl",
"stat5a", "stat5a", "stat5a",
"stat5b", "stat5b", "stat5b"),
id = list.files(path = "kallisto_results/v86")
)
library("DESeq2")## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
## tapply, union, unique, unsplit, which.max, which.min##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:utils':
##
## findMatches## The following objects are masked from 'package:base':
##
## expand.grid, I, unname## Loading required package: IRanges## Loading required package: GenomicRanges## Loading required package: GenomeInfoDb## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:MatrixGenerics':
##
## rowMedians## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMediansdds_v86 <- DESeq2::DESeqDataSetFromMatrix(
countData = as.matrix(cm_v86),
colData = samples_metadata,
design = ~group
)## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' insteaddds_v86## class: DESeqDataSet
## dim: 34983 9
## metadata(1): version
## assays(1): counts
## rownames(34983): ENSG00000000003 ENSG00000000005 ... ENSG00000283697
## ENSG00000283698
## rowData names(0):
## colnames(9): ctrl_r1 ctrl_r2 ... stat5b_r2 stat5b_r3
## colData names(2): group idrowData(dds_v86) <- anno_v86
pcaExplorer::pcaplot(vst(dds_v86), intgroup = "group", ellipse = FALSE)## ## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
dds_v86 <- DESeq(dds_v86)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## estimating size factors## estimating dispersions## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## gene-wise dispersion estimates## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## mean-dispersion relationship## final dispersion estimates## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## fitting model and testing## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' insteadresultsNames(dds_v86)## [1] "Intercept" "group_stat5a_vs_ctrl" "group_stat5b_vs_ctrl"summary(results(dds_v86, name = "group_stat5a_vs_ctrl"), alpha = 0.05)##
## out of 14103 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 380, 2.7%
## LFC < 0 (down) : 285, 2%
## outliers [1] : 1, 0.0071%
## low counts [2] : 10589, 75%
## (mean count < 24)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsDE with latest version
all_samples_v109 <- file.path(
list.files(path = "kallisto_results/v109",
full.names = TRUE),
"gene_expr.csv")
file.exists(all_samples_v109)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEcm_v109 <- data.frame(
ctrl_r1 = read.csv(all_samples_v109[1], sep = ";")$count,
ctrl_r2 = read.csv(all_samples_v109[2], sep = ";")$count,
ctrl_r3 = read.csv(all_samples_v109[3], sep = ";")$count,
stat5a_r1 = read.csv(all_samples_v109[4], sep = ";")$count,
stat5a_r2 = read.csv(all_samples_v109[5], sep = ";")$count,
stat5a_r3 = read.csv(all_samples_v109[6], sep = ";")$count,
stat5b_r1 = read.csv(all_samples_v109[7], sep = ";")$count,
stat5b_r2 = read.csv(all_samples_v109[8], sep = ";")$count,
stat5b_r3 = read.csv(all_samples_v109[9], sep = ";")$count,
row.names = read.csv(all_samples_v109[1], sep = ";")$gene_id
)
anno_v109 <- data.frame(
gene_id = read.csv(all_samples_v109[1], sep = ";")$gene_id,
gene_name = read.csv(all_samples_v109[1], sep = ";")$gene_symbol,
row.names = read.csv(all_samples_v109[1], sep = ";")$gene_id
)
dim(cm_v109)## [1] 35658 9head(cm_v109)## ctrl_r1 ctrl_r2 ctrl_r3 stat5a_r1 stat5a_r2 stat5a_r3 stat5b_r1
## ENSG00000000003 6 3 0 4 2 3 1
## ENSG00000000005 0 0 0 0 0 0 0
## ENSG00000000419 21 13 18 12 25 27 13
## ENSG00000000457 294 214 249 275 390 331 244
## ENSG00000000460 137 110 117 92 102 80 121
## ENSG00000000938 10109 7849 9157 13440 12554 10639 10137
## stat5b_r2 stat5b_r3
## ENSG00000000003 1 2
## ENSG00000000005 0 0
## ENSG00000000419 15 13
## ENSG00000000457 242 305
## ENSG00000000460 144 104
## ENSG00000000938 10890 10165head(anno_v109)## gene_id gene_name
## ENSG00000000003 ENSG00000000003 TSPAN6
## ENSG00000000005 ENSG00000000005 TNMD
## ENSG00000000419 ENSG00000000419 DPM1
## ENSG00000000457 ENSG00000000457 SCYL3
## ENSG00000000460 ENSG00000000460 C1orf112
## ENSG00000000938 ENSG00000000938 FGRdds_v109 <- DESeq2::DESeqDataSetFromMatrix(
countData = as.matrix(cm_v109),
colData = samples_metadata,
design = ~group
)## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' insteaddds_v109## class: DESeqDataSet
## dim: 35658 9
## metadata(1): version
## assays(1): counts
## rownames(35658): ENSG00000000003 ENSG00000000005 ... ENSG00000291316
## ENSG00000291317
## rowData names(0):
## colnames(9): ctrl_r1 ctrl_r2 ... stat5b_r2 stat5b_r3
## colData names(2): group idrowData(dds_v109) <- anno_v109
pcaExplorer::pcaplot(vst(dds_v109), intgroup = "group", ellipse = FALSE)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
dds_v109 <- DESeq(dds_v109)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## estimating size factors## estimating dispersions## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## gene-wise dispersion estimates## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## mean-dispersion relationship## final dispersion estimates## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## fitting model and testing## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' insteadresultsNames(dds_v109)## [1] "Intercept" "group_stat5a_vs_ctrl" "group_stat5b_vs_ctrl"summary(results(dds_v109, name = "group_stat5a_vs_ctrl"), alpha = 0.05)##
## out of 14490 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 367, 2.5%
## LFC < 0 (down) : 270, 1.9%
## outliers [1] : 2, 0.014%
## low counts [2] : 9595, 66%
## (mean count < 11)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultslibrary("patchwork")
pcaExplorer::pcaplot(vst(dds_v86), intgroup = "group", ellipse = FALSE, ntop = 500) |
pcaExplorer::pcaplot(vst(dds_v109), intgroup = "group", ellipse = FALSE, ntop = 500)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
pcaExplorer::pcaplot(vst(dds_v86), intgroup = "group", ellipse = FALSE, ntop = 2000) |
pcaExplorer::pcaplot(vst(dds_v109), intgroup = "group", ellipse = FALSE, ntop = 2000)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
pcaExplorer::pcaplot(vst(dds_v86), intgroup = "group", ellipse = FALSE, ntop = 20000) |
pcaExplorer::pcaplot(vst(dds_v109), intgroup = "group", ellipse = FALSE, ntop = 20000)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
res_v86 <- results(dds_v86, name = "group_stat5a_vs_ctrl")
res_v109 <- results(dds_v109, name = "group_stat5a_vs_ctrl")3 What about the functional level?
We will do some enrichment analyses on the sets above
library("mosdef")
library("org.Hs.eg.db")## Loading required package: AnnotationDbi## res_enrich_v86 <- mosdef::run_cluPro(
dds = dds_v86,
res_de = res_v86,
mapping = "org.Hs.eg.db",
ont = "BP"
)## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## 'select()' returned 1:many mapping between keys and columns## Your dataset has 665 DE genes. You selected 665 (100.00%) genes. You analysed all up_and_down-regulated genesDT::datatable(as.data.frame(res_enrich_v86))res_enrich_v109 <- mosdef::run_cluPro(
dds = dds_v109,
res_de = res_v109,
mapping = "org.Hs.eg.db",
ont = "BP"
)## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## 'select()' returned 1:many mapping between keys and columns## Your dataset has 637 DE genes. You selected 637 (100.00%) genes. You analysed all up_and_down-regulated genesDT::datatable(as.data.frame(res_enrich_v109))Using topGO to try and obtain more targeted and focused categories
library("topGO")## Loading required package: graph## Loading required package: GO.db## Loading required package: SparseM##
## Attaching package: 'SparseM'## The following object is masked from 'package:base':
##
## backsolve##
## groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.##
## Attaching package: 'topGO'## The following object is masked from 'package:IRanges':
##
## membersres_topgo_v86 <- mosdef::run_topGO(
dds = dds_v86,
res_de = res_v86,
mapping = "org.Hs.eg.db",
ont = "BP"
)## 'select()' returned 1:many mapping between keys and columns## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## 'select()' returned 1:many mapping between keys and columns## Your dataset has 665 DE genes. You selected 665 (100.00%) genes. You analysed all up_and_down-regulated genes## 5233 GO terms were analyzed. Not all of them are significantly enriched.
## We suggest further subsetting the output list by for example:
## using a pvalue cutoff in the column:
## 'p.value_elim'.res_topgo_v109 <- mosdef::run_topGO(
dds = dds_v109,
res_de = res_v109,
mapping = "org.Hs.eg.db",
ont = "BP"
)## 'select()' returned 1:many mapping between keys and columns## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## 'select()' returned 1:many mapping between keys and columns## Your dataset has 637 DE genes. You selected 637 (100.00%) genes. You analysed all up_and_down-regulated genes## 5421 GO terms were analyzed. Not all of them are significantly enriched.
## We suggest further subsetting the output list by for example:
## using a pvalue cutoff in the column:
## 'p.value_elim'.DT::datatable(as.data.frame(res_topgo_v86))## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.htmlDT::datatable(as.data.frame(res_topgo_v109))## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.htmlMight be a bit brutal, but let’s try
merged_go_df <- merge(
as.data.frame(res_topgo_v86),
as.data.frame(res_topgo_v109),
by = "GO.ID"
)
plot(merged_go_df$p.value_elim.x,
merged_go_df$p.value_elim.y,
log = "xy")
abline(a = 0, b = 1)
Building up some GeneTonicList object for easy exploration
library("GeneTonic")##
## Attaching package: 'GeneTonic'## The following objects are masked from 'package:mosdef':
##
## deseqresult2df, gene_plot, geneinfo_2_html, go_2_html, map2color,
## styleColorBar_divergentgtl_v86 <- GeneTonicList(
dds_v86,
res_v86,
shake_enrichResult(res_enrich_v86),
annotation_obj = anno_v86
)## Found 4036 gene sets in `enrichResult` object, of which 246 are significant.## Converting for usage in GeneTonic...## ---------------------------------
## ----- GeneTonicList object ------
## ---------------------------------
##
## ----- dds object -----
## Providing an expression object (as DESeqDataset) of 34983 features over 9 samples
##
## ----- res_de object -----
## Providing a DE result object (as DESeqResults), 34983 features tested, 665 found as DE
## Upregulated: 380
## Downregulated: 285
##
## ----- res_enrich object -----
## Providing an enrichment result object, 4036 reported
##
## ----- annotation_obj object -----
## Providing an annotation object of 34983 features with information on 2 identifier typesgtl_v109 <- GeneTonicList(
dds_v109,
res_v109,
shake_enrichResult(res_enrich_v109),
annotation_obj = anno_v109
)## Found 4096 gene sets in `enrichResult` object, of which 286 are significant.## Converting for usage in GeneTonic...## ---------------------------------
## ----- GeneTonicList object ------
## ---------------------------------
##
## ----- dds object -----
## Providing an expression object (as DESeqDataset) of 35658 features over 9 samples
##
## ----- res_de object -----
## Providing a DE result object (as DESeqResults), 35658 features tested, 637 found as DE
## Upregulated: 367
## Downregulated: 270
##
## ----- res_enrich object -----
## Providing an enrichment result object, 4096 reported
##
## ----- annotation_obj object -----
## Providing an annotation object of 35658 features with information on 2 identifier typesA more quanti-quali tative approach would be with a representative heatmap
vst_v86 <- vst(dds_v86)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead## Warning in as.data.frame.numeric(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' insteadvst_v109 <- vst(dds_v109)## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' insteadGeneTonic::gs_heatmap(vst_v86,
gtl = gtl_v86,
geneset_id = "GO:0031640",
anno_col_info = "group")## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
GeneTonic::gs_heatmap(vst_v109,
gtl = gtl_v109,
geneset_id = "GO:0031640",
anno_col_info = "group")## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.factor()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.factor(col, optional = optional): Direct call of
## 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
Session info
sessionInfo()## R Under development (unstable) (2024-03-12 r86109)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GeneTonic_2.7.2 topGO_2.55.0
## [3] SparseM_1.81 GO.db_3.18.0
## [5] graph_1.81.0 org.Hs.eg.db_3.18.0
## [7] AnnotationDbi_1.65.2 mosdef_0.99.0
## [9] patchwork_1.2.0 bigmemory_4.6.4
## [11] DESeq2_1.43.4 SummarizedExperiment_1.33.3
## [13] Biobase_2.63.0 MatrixGenerics_1.15.0
## [15] matrixStats_1.2.0 GenomicRanges_1.55.3
## [17] GenomeInfoDb_1.39.9 IRanges_2.37.1
## [19] S4Vectors_0.41.4 BiocGenerics_0.49.1
## [21] ggplot2_3.5.0
##
## loaded via a namespace (and not attached):
## [1] GSEABase_1.65.1 progress_1.2.3 DT_0.32
## [4] Biostrings_2.71.4 vctrs_0.6.5 digest_0.6.35
## [7] png_0.1-8 shape_1.4.6.1 shinyBS_0.61.1
## [10] registry_0.5-1 ggrepel_0.9.5 magick_2.8.3
## [13] MASS_7.3-60.2 reshape2_1.4.4 httpuv_1.6.14
## [16] foreach_1.5.2 qvalue_2.35.0 withr_3.0.0
## [19] xfun_0.42 ggfun_0.1.4 ellipsis_0.3.2
## [22] survival_3.5-8 memoise_2.0.1 clusterProfiler_4.11.0
## [25] gson_0.1.0 BiasedUrn_2.0.11 tidytree_0.4.6
## [28] GlobalOptions_0.1.2 gtools_3.9.5 prettyunits_1.2.0
## [31] KEGGREST_1.43.0 promises_1.2.1 httr_1.4.7
## [34] restfulr_0.0.15 rstudioapi_0.15.0 shinyAce_0.4.2
## [37] miniUI_0.1.1.1 generics_0.1.3 DOSE_3.29.2
## [40] base64enc_0.1-3 curl_5.2.1 zlibbioc_1.49.3
## [43] ggraph_2.2.1 polyclip_1.10-6 ca_0.71.1
## [46] GenomeInfoDbData_1.2.11 SparseArray_1.3.4 RBGL_1.79.0
## [49] threejs_0.3.3 xtable_1.8-4 stringr_1.5.1
## [52] doParallel_1.0.17 evaluate_0.23 S4Arrays_1.3.6
## [55] BiocFileCache_2.11.1 hms_1.1.3 colorspace_2.1-0
## [58] filelock_1.0.3 visNetwork_2.1.2 pcaExplorer_2.29.0
## [61] shinyWidgets_0.8.2 magrittr_2.0.3 Rgraphviz_2.47.0
## [64] later_1.3.2 viridis_0.6.5 ggtree_3.11.1
## [67] lattice_0.22-5 NMF_0.27 genefilter_1.85.1
## [70] XML_3.99-0.16.1 shadowtext_0.1.3 cowplot_1.1.3
## [73] pillar_1.9.0 nlme_3.1-164 iterators_1.0.14
## [76] gridBase_0.4-7 caTools_1.18.2 compiler_4.4.0
## [79] stringi_1.8.3 shinycssloaders_1.0.0 Category_2.69.0
## [82] TSP_1.2-4 dendextend_1.17.1 GenomicAlignments_1.39.4
## [85] plyr_1.8.9 crayon_1.5.2 abind_1.4-5
## [88] BiocIO_1.13.0 gridGraphics_0.5-1 locfit_1.5-9.9
## [91] graphlayouts_1.1.1 bit_4.0.5 UpSetR_1.4.0
## [94] dplyr_1.1.4 fastmatch_1.1-4 codetools_0.2-19
## [97] crosstalk_1.2.1 bslib_0.6.1 GetoptLong_1.0.5
## [100] plotly_4.10.4 mime_0.12 splines_4.4.0
## [103] circlize_0.4.16 Rcpp_1.0.12 dbplyr_2.4.0
## [106] tippy_0.1.0 HDO.db_0.99.1 knitr_1.45
## [109] blob_1.2.4 utf8_1.2.4 clue_0.3-65
## [112] BiocVersion_3.19.1 fs_1.6.3 backbone_2.1.3
## [115] expm_0.999-9 ggplotify_0.1.2 tibble_3.2.1
## [118] Matrix_1.6-5 statmod_1.5.0 tweenr_2.0.3
## [121] pkgconfig_2.0.3 pheatmap_1.0.12 tools_4.4.0
## [124] cachem_1.0.8 RSQLite_2.3.5 viridisLite_0.4.2
## [127] DBI_1.2.2 fastmap_1.1.1 rmarkdown_2.26
## [130] scales_1.3.0 grid_4.4.0 shinydashboard_0.7.2
## [133] Rsamtools_2.19.3 AnnotationHub_3.11.1 sass_0.4.8
## [136] BiocManager_1.30.22 farver_2.1.1 tidygraph_1.3.1
## [139] scatterpie_0.2.1 mgcv_1.9-1 yaml_2.3.8
## [142] AnnotationForge_1.45.0 rtracklayer_1.63.1 cli_3.6.2
## [145] purrr_1.0.2 webshot_0.5.5 lifecycle_1.0.4
## [148] rintrojs_0.3.4 BiocParallel_1.37.1 annotate_1.81.2
## [151] gtable_0.3.4 rjson_0.2.21 ggridges_0.5.6
## [154] parallel_4.4.0 ape_5.7-1 limma_3.59.5
## [157] jsonlite_1.8.8 colourpicker_1.3.0 seriation_1.5.4
## [160] bitops_1.0-7 bigmemory.sri_0.1.8 bit64_4.0.5
## [163] assertthat_0.2.1 yulab.utils_0.1.4 heatmaply_1.5.0
## [166] geneLenDataBase_1.39.0 bs4Dash_2.3.3 jquerylib_0.1.4
## [169] highr_0.10 GOSemSim_2.29.1 lazyeval_0.2.2
## [172] shiny_1.8.0 dynamicTreeCut_1.63-1 htmltools_0.5.7
## [175] enrichplot_1.23.1 rappdirs_0.3.3 glue_1.7.0
## [178] httr2_1.0.0 XVector_0.43.1 RCurl_1.98-1.14
## [181] treeio_1.27.0 ComplexUpset_1.3.3 gridExtra_2.3
## [184] igraph_2.0.3 R6_2.5.1 tidyr_1.3.1
## [187] gplots_3.1.3.1 labeling_0.4.3 GenomicFeatures_1.55.4
## [190] cluster_2.1.6 rngtools_1.5.2 aplot_0.2.2
## [193] DelayedArray_0.29.9 tidyselect_1.2.1 GOstats_2.69.0
## [196] ggforce_0.4.2 xml2_1.3.6 munsell_0.5.0
## [199] KernSmooth_2.23-22 goseq_1.55.0 data.table_1.15.2
## [202] htmlwidgets_1.6.4 fgsea_1.29.0 ComplexHeatmap_2.19.0
## [205] RColorBrewer_1.1-3 biomaRt_2.59.1 rlang_1.1.3
## [208] uuid_1.2-0 fansi_1.0.6 Cairo_1.6-2